| Summary: | An automated-based intelligence approaches have widely used for quantifying In-Vitro Fertilisation (IVF) blastocyst image features that offer automation in morphology assessment as well as embryo selection to improve embryo implantation. Since the IVF blastocyst co-existed three main features of Zona Pellucida (ZP), Trophectoderm (TE) and Inner Cell Mass (ICM), this has made it crucial to consider the informative regions of all features in image morphology assessment. Although the implementation of Navigator-Teacher-Scrutinizer Network (NTS-net) has been detected most informative regions under the guidance of the Teacher network, there still limitation on calculation of the feature extraction process of different blastocyst features that led to poor classification performance. Therefore, this study proposes a new classification model namely NTS-CAM to improve extracted blastocyst features by assigning weights to channel features in channel attention mechanism (CAM) while extracting informative regions of each blastocyst feature. The benchmarking dataset showed significant performance of classification accuracy for ZP, TE, and ICM features with 80.5 %, 67.4 %, and 76.3 %, and the clinical dataset showed 74.1 %, 71.8 %, and 63.5 %, respectively. In conclusion, the proposed NTS-CAM model to predict grade of IVF blastocyst quality has improved the performance compared to classic NTS model. Furthermore, the improved model can be used for clinical decision making as well as for quality control in IVF procedure. © 2024 Elsevier GmbH
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